




'''simple docstring'''


from typing import List, Optional, Union

import torch

from ...models import UNetaDConditionModel, VQModel
from ...pipelines import DiffusionPipeline
from ...pipelines.pipeline_utils import ImagePipelineOutput
from ...schedulers import DDPMScheduler
from ...utils import (
    is_accelerate_available,
    is_accelerate_version,
    logging,
    randn_tensor,
    replace_example_docstring,
)


UpperCAmelCase_					=   logging.get_logger(__name__)  # pylint: disable=invalid-name

UpperCAmelCase_					=   "\n    Examples:\n        ```py\n        >>> from diffusers import KandinskyV22Pipeline, KandinskyV22PriorPipeline\n        >>> import torch\n\n        >>> pipe_prior = KandinskyV22PriorPipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-prior\")\n        >>> pipe_prior.to(\"cuda\")\n        >>> prompt = \"red cat, 4k photo\"\n        >>> out = pipe_prior(prompt)\n        >>> image_emb = out.image_embeds\n        >>> zero_image_emb = out.negative_image_embeds\n        >>> pipe = KandinskyV22Pipeline.from_pretrained(\"kandinsky-community/kandinsky-2-2-decoder\")\n        >>> pipe.to(\"cuda\")\n        >>> image = pipe(\n        ...     image_embeds=image_emb,\n        ...     negative_image_embeds=zero_image_emb,\n        ...     height=768,\n        ...     width=768,\n        ...     num_inference_steps=50,\n        ... ).images\n        >>> image[0].save(\"cat.png\")\n        ```\n"


def     SCREAMING_SNAKE_CASE							(   a_						:				Any					,       a_						:				Any					,       a_						:				str=8      ):
  __a								=       height // scale_factor**2
  if height % scale_factor**2 != 0:
    new_height += 1
  __a								=       width // scale_factor**2
  if width % scale_factor**2 != 0:
    new_width += 1
  return new_height * scale_factor, new_width * scale_factor





class  __lowercase    (	__magic_name__			):







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				)					->     Optional[int]:
     super().__init__()

     self.register_modules(
         unet=UpperCamelCase   ,				scheduler=UpperCamelCase   ,				movq=UpperCamelCase   ,				)
     __a								=       2 ** (len(self.movq.config.block_out_channels   ) - 1)







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     Any:
     if latents is None:
       __a								=       randn_tensor(UpperCamelCase   ,				generator=UpperCamelCase   ,				device=UpperCamelCase   ,				dtype=UpperCamelCase   )
     else:
       if latents.shape != shape:
         raise ValueError(f"Unexpected latents shape, got {latents.shape}, expected {shape}"   )
       __a								=       latents.to(UpperCamelCase   )

     __a								=       latents * scheduler.init_noise_sigma
     return latents







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     str:
     if is_accelerate_available():
       from accelerate import cpu_offload
     else:
       raise ImportError('Please install accelerate via `pip install accelerate`'   )

     __a								=       torch.device(f"cuda:{gpu_id}"   )

     __a								=       [
         self.unet,
         self.movq,
     ]
     for cpu_offloaded_model in models:
       if cpu_offloaded_model is not None:
         cpu_offload(UpperCamelCase   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase=0   )					->     Optional[int]:
     if is_accelerate_available() and is_accelerate_version('>='   ,				'0.17.0.dev0'   ):
       from accelerate import cpu_offload_with_hook
     else:
       raise ImportError('`enable_model_cpu_offload` requires `accelerate v0.17.0` or higher.'   )

     __a								=       torch.device(f"cuda:{gpu_id}"   )

     if self.device.type != "cpu":
       self.to('cpu'   ,				silence_dtype_warnings=UpperCamelCase   )
       torch.cuda.empty_cache()  # otherwise we don't see the memory savings (but they probably exist)

     __a								=       None
     for cpu_offloaded_model in [self.unet, self.movq]:
       __a						,							__a								=       cpu_offload_with_hook(UpperCamelCase   ,				UpperCamelCase   ,				prev_module_hook=UpperCamelCase   )

     # We'll offload the last model manually.
     __a								=       hook







   @property
   # Copied from diffusers.pipelines.stable_diffusion.pipeline_stable_diffusion.StableDiffusionPipeline._execution_device
   def 		UpperCamelCase__      (						self   )					->     Tuple:
     if not hasattr(self.unet   ,				'_hf_hook'   ):
       return self.device
     for module in self.unet.modules():
       if (
           hasattr(UpperCamelCase   ,				'_hf_hook'   )
           and hasattr(module._hf_hook   ,				'execution_device'   )
           and module._hf_hook.execution_device is not None
       ):
         return torch.device(module._hf_hook.execution_device   )
     return self.device







   @torch.no_grad()
   @replace_example_docstring(UpperCamelCase   )
   def __call__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase = 512   ,				UpperCamelCase = 512   ,				UpperCamelCase = 100   ,				UpperCamelCase = 4.0   ,				UpperCamelCase = 1   ,				UpperCamelCase = None   ,				UpperCamelCase = None   ,				UpperCamelCase = "pil"   ,				UpperCamelCase = True   ,				)					->     Dict:
     __a								=       self._execution_device

     __a								=       guidance_scale > 1.0

     if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
       __a								=       torch.cat(UpperCamelCase   ,				dim=0   )
     __a								=       image_embeds.shape[0] * num_images_per_prompt
     if isinstance(UpperCamelCase   ,				UpperCamelCase   ):
       __a								=       torch.cat(UpperCamelCase   ,				dim=0   )

     if do_classifier_free_guidance:
       __a								=       image_embeds.repeat_interleave(UpperCamelCase   ,				dim=0   )
       __a								=       negative_image_embeds.repeat_interleave(UpperCamelCase   ,				dim=0   )

     __a								=       torch.cat([negative_image_embeds, image_embeds]   ,				dim=0   ).to(dtype=self.unet.dtype   ,				device=UpperCamelCase   )

     self.scheduler.set_timesteps(UpperCamelCase   ,				device=UpperCamelCase   )
     __a								=       self.scheduler.timesteps

     __a								=       self.unet.config.in_channels

     __a						,							__a								=       downscale_height_and_width(UpperCamelCase   ,				UpperCamelCase   ,				self.movq_scale_factor   )

     # create initial latent
     __a								=       self.prepare_latents(
         (batch_size, num_channels_latents, height, width)   ,				image_embeds.dtype   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				self.scheduler   ,				)

     for i, t in enumerate(self.progress_bar(UpperCamelCase   )   ):
       # expand the latents if we are doing classifier free guidance
       __a								=       torch.cat([latents] * 2   ) if do_classifier_free_guidance else latents

       __a								=       {'image_embeds': image_embeds}
       __a								=       self.unet(
           sample=UpperCamelCase   ,				timestep=UpperCamelCase   ,				encoder_hidden_states=UpperCamelCase   ,				added_cond_kwargs=UpperCamelCase   ,				return_dict=UpperCamelCase   ,				)[0]

       if do_classifier_free_guidance:
         __a						,							__a								=       noise_pred.split(latents.shape[1]   ,				dim=1   )
         __a						,							__a								=       noise_pred.chunk(2   )
         __a						,							__a								=       variance_pred.chunk(2   )
         __a								=       noise_pred_uncond + guidance_scale * (noise_pred_text - noise_pred_uncond)
         __a								=       torch.cat([noise_pred, variance_pred_text]   ,				dim=1   )

       if not (
           hasattr(self.scheduler.config   ,				'variance_type'   )
           and self.scheduler.config.variance_type in ["learned", "learned_range"]
       ):
         __a						,							__a								=       noise_pred.split(latents.shape[1]   ,				dim=1   )

       # compute the previous noisy sample x_t -> x_t-1
       __a								=       self.scheduler.step(
           UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   ,				generator=UpperCamelCase   ,				)[0]
     # post-processing
     __a								=       self.movq.decode(UpperCamelCase   ,				force_not_quantize=UpperCamelCase   )['sample']

     if output_type not in ["pt", "np", "pil"]:
       raise ValueError(f"Only the output types `pt`, `pil` and `np` are supported not output_type={output_type}"   )

     if output_type in ["np", "pil"]:
       __a								=       image * 0.5 + 0.5
       __a								=       image.clamp(0   ,				1   )
       __a								=       image.cpu().permute(0   ,				2   ,				3   ,				1   ).float().numpy()

     if output_type == "pil":
       __a								=       self.numpy_to_pil(UpperCamelCase   )

     if not return_dict:
       return (image,)

     return ImagePipelineOutput(images=UpperCamelCase   )









'''simple docstring'''


class  __lowercase    :  # Public class to implement a graph







   def __init__(						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     __a								=       row
     __a								=       col
     __a								=       graph







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     bool:
     return (
         0 <= i < self.ROW
         and 0 <= j < self.COL
         and not visited[i][j]
         and self.graph[i][j]
     )







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )					->     None:
     # Checking all 8 elements surrounding nth element
     __a								=       [-1, -1, -1, 0, 0, 1, 1, 1]  # Coordinate order
     __a								=       [-1, 0, 1, -1, 1, -1, 0, 1]
     __a								=       True  # Make those cells visited
     for k in range(8   ):
       if self.is_safe(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   ):
         self.diffs(i + row_nbr[k]   ,				j + col_nbr[k]   ,				UpperCamelCase   )







   def 		UpperCamelCase__      (						self   )					->     int:  # And finally, count all islands.
     __a								=       [[False for j in range(self.COL   )] for i in range(self.ROW   )]
     __a								=       0
     for i in range(self.ROW   ):
       for j in range(self.COL   ):
         if visited[i][j] is False and self.graph[i][j] == 1:
           self.diffs(UpperCamelCase   ,				UpperCamelCase   ,				UpperCamelCase   )
           count += 1
     return count



